Potential impacts of environmental bacteria on the microbiota of loggerhead (Caretta caretta) and green (Chelonia mydas) sea turtle eggs and their hatching success

Abstract Sea turtle hatching success can be affected by many variables, including pathogenic microbes, but it is unclear which microbes are most impactful and how they are transmitted into the eggs. This study characterized and compared the bacterial communities from the (i) cloaca of nesting sea turtles (ii) sand within and surrounding the nests; and (iii) hatched and unhatched eggshells from loggerhead (Caretta caretta) and green (Chelonia mydas) turtles. High throughput sequencing of bacterial 16S ribosomal RNA gene V4 region amplicons was performed on samples collected from 27 total nests in Fort Lauderdale and Hillsboro beaches in southeast Florida, United States. Significant differences were identified between hatched and unhatched egg microbiota with the differences caused predominately by Pseudomonas spp., found in higher abundances in unhatched eggs (19.29% relative abundance) than hatched eggs (1.10% relative abundance). Microbiota similarities indicate that the nest sand environment, particularly nest distance from dunes, played a larger role than the nesting mother's cloaca in influencing hatched and unhatched egg microbiota. Pathogenic bacteria potentially derive from mixed‐mode transmission or additional sources not included in this study as suggested by the high proportion (24%–48%) of unhatched egg microbiota derived from unknown sources. Nonetheless, the results suggest Pseudomonas as a candidate pathogen or opportunistic colonizer associated with sea turtle egg‐hatching failure.

reproductive output to compensate for high natural mortality during early developmental stages (Butler, 1997;Wyneken et al., 1988). The eggs are fertilized internally, developing a soft shell before oviposition (Owens, 1980). After fertilization, females nest on sandy beaches depositing two to seven clutches of around 100 eggs per season (Carr, 1967). The eggs then incubate within the nest for 40-60 days, after which the hatchlings synchronize for a nighttime emergence (Carr & Hirth, 1961).
Survival rates of sea turtle eggs and hatchlings have decreased by different factors: physical nest destruction, predation, poaching, abiotic nest conditions (e.g., temperature, gas exchange, and moisture), and microbial interactions (Honarvar et al., 2011).
Microbes live symbiotically with most eukaryotic organisms throughout their lifecycle (McFall-Ngai et al., 2013), and this includes sea turtles from egg formation up to and throughout adulthood. The primary mechanisms for microbial introduction are hypothesized to be through either (i) maternal transmission during the two-week formation period within the uterine tube and oviposition (Funkhouser & Bordenstein, 2013) or (ii) environmental transmission from sand surrounding the nest (Craven et al., 2007).
Maternal transmission is considered one of the main drivers of microbial introduction to sea turtle eggs. Sea turtle eggs spend the majority of their development (approximately two months) incubating in the sand without paternal care, therefore maternal transmission of bacteria can only occur during the two-week formation period before oviposition (Miller, 1985). Sea turtle hatchlings have been found to acquire a portion of their normal flora from the mother before oviposition (Scheelings, 2019). The cloaca can potentially also introduce infectious microbes due to its function as a combined opening for the digestive, urinary, and reproductive systems in birds, reptiles, and amphibians. The turtle cloaca has been shown to hold a highly complex and variable microbiome (Al-Bahry et al., 2009).
Reproductive behaviors, environmental conditions, and diet can expose the cloaca to new and potentially pathogenic microbes.
However, the establishment of new microbes in the cloaca depends on the presiding microbial community structure, the host's immune system, and the cloacal environment (Poiani, 2010). Additionally, mucus excreted during oviposition may contain antimicrobial properties that protect the embryos from potential pathogens in the environment or the reproductive tract (Keene, 2012;Praja et al., 2021;Soslau et al., 2011). This suggests that the transmission of microbes could occur at any time, whether it be before deposition (from the mother) or later on in the incubation process (from the environment).
Environmental transmission is considered another main driver of microbial introduction due to eggshell characteristics (Craven et al., 2007;De Reu et al., 2006). Sea turtles have a flexible eggshell composed of an inner organic membrane and an outer inorganic, calcareous layer that both contain numerous openings allowing for moisture and respiratory gas transfer (Al-Bahry et al., 2009;Chan & Solomon, 1989). Environmental conditions in nests (e.g., temperature and oxygen content) have been found to correlate with the microbial assemblage present in the nest and with overall hatching success (Bézy et al., 2015). Physical properties of the nest substrate (e.g., sand grain size and organic matter content) establish diffusion rates of gases (e.g., oxygen and carbon dioxide), transport of water, and transmission of heat throughout the egg chamber, affecting developmental rates of the egg clutch (Ackerman, 1996;Mortimer, 1990). Biotic factors (e.g., clutch size and microbial activity) can alter nest temperature and gas composition, which can indirectly affect hatching success (Ackerman, 1996;Bézy et al., 2015;Mortimer, 1990;Prange & Ackerman, 1974).
Beach sands harbor diverse microbiomes that provide important ecosystem services, such as water purification, biogeochemical cycling, and organic compound mineralization (Boehm et al., 2014).
Geographic and tidal zone locations, sand depth, water pollution, atmospheric deposition, and anthropogenic activity have been found to cause sand microbial variation between beaches (Boehm et al., 2014;Hu et al., 2019;Piggot et al., 2012;Staley & Sadowsky, 2016). Sand microbes from within sea turtle nests can also differ from the beach sand microbes due to the mucus excreted by the female providing a comparatively warm, moist environment and potential nutrient resource that promotes the growth of certain microbes within the egg chamber (Honarvar et al., 2011;Wyneken et al., 1988). When hatched and unhatched (also referred to as "failed") egg microbiomes were previously characterized, differences in microbial species and overall microbial abundances were found to impact hatching success. Higher bacterial and fungal abundances and diversity in eggs (Gifari et al., 2018;Wyneken et al., 1988) and sand (Bézy et al., 2015;Honarvar et al., 2011) have been found to negatively impact the hatch rate success of a nest.
Research in sea turtle egg hatchability and their association with pathogens has been focused on sea turtle egg fusariosis (STEF) and Fusarium solani species complex (FSSC), which are fungal diseases causing high egg mortality in sea turtle nests worldwide (Gleason et al., 2020;Rosado-Rodríguez & Maldonado-Ramírez, 2016;Sarmiento-Ramírez et al., 2010Sidique et al., 2017;Smyth et al., 2019). STEF and FSSC are not the only fungi capable of penetrating and infecting sea turtle eggs. Additional fungal species identified in sea turtle eggs include Aspergillus, Emericella, Rhizopus, Actinomucor, and Apophysomyces (Candan, 2018). With research mostly focused on the fungal diseases impacting sea turtle hatching success, there is a lack of information on the role of bacteria within sea turtle eggs.  provided the first analysis of bacterial composition in sea turtle eggs using PhyloChip methods. However, their study had a limited sampling size of four eggs (two hatched and two unhatched) collected from two nests and was focused on Fusarium-infected nests. More research using new culture-independent methods (e.g., massive parallel sequencing) and larger sampling efforts need to be conducted on uninfected nests to help determine the potential role of bacteria in sea turtle egg development.
To date, only a few studies have utilized high throughput sequencing (HTS) of metagenomic DNA to characterize sea turtle egg microbiota and correlate it with hatching success. Scheelings (2019) used HTS to determine how sea turtle hatchlings acquire their normal flora by comparing bacterial composition between freshly laid eggs and blood and gut samples from nesting mothers and hatchlings.
Their results found that the eggs share 44% of their microbiota with the mother's blood, suggesting that a portion of their microbiota is transferred before shell formation (Scheelings, 2019). Hoh et al. (2020) used HTS to compare hatchery practices by the differences in bacterial and fungal pathogens found in the sand and Fusariuminfected eggs. Their results found that eggs suffer an increased risk of infection in hatcheries that reuse sand for several nesting seasons (Hoh et al., 2020). Bézy et al. (2020) used HTS to compare the microbial composition in nest sand between areas of different embryo survivorship on a Costa Rica arribada beach but did not investigate the egg and cloaca microbiota. Their results found that sand microbial composition corresponds to particular environmental conditions suggesting that the presence of pathogenic microbes alone cannot fully determine hatching success (Bézy et al., 2020). Vecchioni et al. (2022) used HTS to characterize and compare loggerhead sea turtle egg microbiota along the Italian coasts of the Mediterranean Sea. Their results suggested that egg microbiota are shaped by maternal and environmental influences alongside a protective role of eggshells, but their study lacked microbial sampling from the nesting females to validate this claim (Vecchioni et al., 2022). Capri et al. (2023) used HTS and culture-dependent methods to determine the bacteria and fungi responsible for hatching failure in two green sea turtle nests with different hatching success rates (0% and 59%). Their results found that differences in bacterial abundance may have a more predominant role in hatching success than fungi.
Additionally, their results found that Pseudomonas and Brucella were the main bacteria affecting hatching success. Capri et al. (2023) also hypothesized that Pseudomonas derived from the sand while Brucella from the nesting female, however, their study lacks bacterial analysis from the nesting female to support this claim.
The primary aim of our study was to characterize the bacterial composition of hatched and unhatched eggs for two important turtle species, the loggerhead (Caretta caretta) and green (Chelonia mydas) turtles in southeast Florida, and compare them with samples taken from the cloaca of the nesting sea turtles and from the sand surrounding and within the nests. Identifying microbial differences between hatched and unhatched eggs from loggerhead and green turtles will lead to additional insights into the potential roles of bacteria in sea turtle eggs (e.g., commensals or pathogens). Comparisons between cloaca, sand, and egg samples will also provide new information on the possible transmission source for sea turtle egg bacterial microbiota. Florida and serves as a consistent annual nesting site for loggerheads (C. caretta), green turtles (C. mydas), and to a lesser extent leatherbacks (Dermochelys coriacea), which constitute approximately 90%-95%, 5%, and 1% of local nesting, respectively (Burkholder et al., 2020).
A total of nine different samples were collected from each nest (one cloaca, two sand, and six egg samples) resulting in 243 total samples being collected. Cloaca swabs were collected after oviposition while the turtle was temporarily detained by BCSTCP staff for research purposes (e.g., tagging and isotope and genetic sampling). A BD BBL™ CultureSwab™ EZ sterile media-free polyurethane foam swab was dipped into sterile water and inserted into the cloaca (about 5-6 cm or no further than the point of resistance) where the epithelium was gently scraped for about 10 s. The remaining samples were collected following the nest excavation by the BCSTCP staff under MTP #214, which occurred three days after the nest hatch-out had been documented following FWC Marine Turtle Guidelines. Two sand samples were collected from each nest using a sterile 2 mL microcentrifuge collection tube: the first ("nest sand") was collected from the compact sand found at the bottom of the egg chamber, while the second ("control sand") was collected from a human dug area of sand approximately 1-2 m away from the nest at the same depth as the egg chamber (range = 41-80 cm; Table 1). The latter sample was meant to represent sand samples with no association to any sea turtle nests (the original beach bacterial community). Within each nest, three hatched eggs and three unhatched eggs were swabbed on the interior portion of the eggshell for up to 30 s. Only hatched eggs that had more than 50% of the eggshell intact after hatchling emergence were selected since the interior could easily be distinguished from the exterior side. These hatched eggs also had a reduced chance of sand bacterial contamination affecting the eggshell's interior bacterial community due to the collapsing of the shell structure after turtle emergence. Only unhatched eggs that were not fully white, turgid, or demonstrated other characteristics suggestive of the presence of a living embryo were opened for swabbing. Eggs that were determined to be potentially viable were returned to the nest and reburied. All samples were placed on ice and transported to the laboratory to be stored at −80°C until DNA extractions began.
Environmental metadata were collected following standards set by Knight et al. (2012) Sand samples were fractionated with a set of sieves (63,125,250,500,2000, and 4000 microns) to determine the particle-size distribution by mass. The "G2Sd" R package (Fournier et al., 2014) was used to calculate the mean of the grain-size distribution (geometric method of moments; Bunte & Abt, 2001) and sorting coefficient using the Trask Index (Trask, 1932)  Note: Nest number indicates the nest number associated with the nest sampled on each beach, as determined by the BCSTCP. Turtle species (Loggerhead or Green Turtle), beach (Fort Lauderdale or Hillsboro), and GPS location (latitude and longitude) associated with each nest number are identified. Bacterial incubation length (days) was determined by the number of days between the date the nests were originally laid and the date of excavation. Hatching success was determined by dividing the number of hatched eggs (total number of hatched, LPIP, and DPIP eggs) by the total clutch size (total number of hatched, LPIP, DPIP, and whole eggs). Chamber depth was measured after all nest contents were removed, measuring from the bottom of the egg chamber to the topmost point of leveled sand. High tide and dune distance (m) were measured by BCSTCP staff when the nest was originally laid. Washover occurrences (50% of the nest was exposed to tidal action) were observed by BCSTCP staff throughout each nest's incubation period, total amounts of occurrences were calculated after nest removal. Conductivity, temperature, and pH were collected from the side and bottoms of each nest (both values shown in the table) immediately after nest contents were removed. Sand grain size and sorting coefficients were determined using 75 g of sand collected from within each egg chamber. Abbreviations: BCSTCP, Broward County Sea Turtle Conservation Program; DPIP, dead pipped hatchlings; LPIP, live pipped hatchlings.

| Data analysis
The Quantitative Insights into Microbial Ecology v.2 (QIIME 2 v.2021.8) pipeline was used to perform microbial bioinformatics after sequencing was completed (Bolyen et al., 2019). Raw sequences were demultiplexed and quality-filtered using DADA2 (Callahan et al., 2016). Amplicon sequence variants (ASV) were aligned with MAFFT (Katoh et al., 2002) and used to construct phylogeny with fasttree2 (Price et al., 2010). Taxonomy was assigned to the ASVs using the SILVA 138.1 feature classifier for the 515F/806R region of sequences (Quast et al., 2012;Yilmaz et al., 2014) after it was trained using scikit-learn 0.24.1 (Bokulich et al., 2018). Before analysis, the feature table created by QIIME 2 was cleaned using the "decontam" ASVs. A final cleaning check was performed using the "vegan" package (Oksanen et al., 2020) where ASVs occurring less than 0.001% were removed, resulting in two additional ASVs being removed. Low threshold and abundance levels were used to preserve the bacterial diversity in the samples.
Alpha diversity, including Margalef's species richness (Margalef, 1958), Shannon diversity (Shannon & Weaver, 1949), and Inverse Simpson's diversity (Simpson, 1949), was assessed for each sample in the dataset. Generalized linear mixed-effect models (GLMM) with gaussian distribution were performed using the "lme4" R package (Bates et al., 2015) for all samples to determine which variables affected each alpha diversity metric (response variables).
Before setting up the model, the variables were assessed for covariation. Variables with strong correlations included latitudelongitude (corr = 0.996), latitude-dune distance (corr = −0.791), included the nest number as a random effect to control for lack of independence between eggs from the same nest. Final "best-fit" models were determined through term-selection by AIC score comparisons. If the removal of a variable caused the AIC score to increase by a value of two or more than that variable was kept for the "best-fit" model. The "best-fit" models were validated by plotting Pearson residuals against fitted values, against each variable (covariate) in the model, and against each variable (covariate) not in the model. If the interaction terms were kept in the "best-fit" model, then the "emmeans" package (Lenth, 2023) was used to perform pairwise analyses to determine which levels of the categorical variables had a significant effect.
To assess if sampling effort affected the differences in alpha diversity metrics between turtle species and beaches, sample-sizebased rarefaction (interpolation) and prediction (extrapolation) curves were created using the "iNEXT" R package (Hsieh et al., 2016) by computing diversity estimates using the number of samples with respect to the total number of individuals (ASVs). These analyses (and future beta diversity analyses) were completed for each turtle species and beach separately. Comparisons between beaches were only made using loggerhead turtle samples, due to the low number of green turtle nests sampled at Fort Lauderdale (n = 2).
The data were then converted into relative abundance (resulting in proportional data for each sample) to perform beta diversity analyses between turtle species and beaches. Bray-Curtis dissimilarity matrices with ASVs standardized by sample total were used to generate nonmetric multidimensional scaling plots using PRIMER v.7.0.17 software (Clarke & Gorley, 2015). One-way, unordered analysis of similarity (ANOSIM; Clarke, 1993) with 9999 permutations was used to determine whether there were differences in community composition between sample types (hatched eggs, unhatched eggs, nest sand, control sand, and cloaca), beaches, and turtle species. Where significant differences were detected between groups, Similarity Percentages (SIMPER) analyses (Rees et al., 2004) with 9999 permutations were then used to determine which bacterial taxa were significantly different between sample types, beaches, and turtle species. Hatched and unhatched egg microbiota were compared to sand and cloaca microbiota to determine potential transmission sources using the "venn" R package (Dusa, 2021) to determine how many unique ASVs hatched and unhatched eggs had in common with cloaca and sand samples. SourceTracker (Knights et al., 2011) was then used to estimate the proportional contribution of each source type for hatched and unhatched egg microbiota using Bayesian modeling for proposed known (cloaca, control sand, and nest sand samples) and unknown source environments. An additional SourceTracker analysis was used to estimate the proportional contribution of control sand and cloaca microbiota on nest sand microbiota.
Differences between sample types were further visualized in R with the "phyloseq" and "microbiome" (Lahti & Shetty, 2019) packages using the relative abundances of the most abundant phyla and genera. Canonical analysis of principal coordinates (CAP; Anderson & Willis, 2003)

| Bacterial community compositions
Control sand samples had significantly greater bacterial taxa richness and diversity when compared to egg and cloaca sample types in loggerhead and Hillsboro samples ( Figure 1 and Table A2). Nest sand samples were significantly greater in bacterial taxa richness and diversity for both turtle species and beaches only when compared to hatched and unhatched eggs ( Figure 1 and Table A2). Between egg sample types, hatched eggs had greater bacterial taxa richness and diversity than unhatched eggs ( Figure 1 and Table A2). Loggerhead cloaca and control sand samples were significantly greater in bacterial richness and diversity than green turtles ( Figure 1 and Table A3), while green turtle unhatched egg samples were significantly greater in diversity than loggerheads ( Figure 1 and Table A3). Hillsboro control sand samples were significantly greater in bacterial richness and diversity than Fort Lauderdale ( Figure 1 and Table A3). Although more samples were collected from loggerhead nests, these trends remained consistent when sample-size was taken into consideration ( Figures A1 and A2). One-way ANOSIM tests showed that there were significant differences between the sample types in separate analyses for each turtle species (Loggerhead: R = 0.493, p < 0.001; Green Turtle: R = 0.154, p = 0.006) and each beach (Hillsboro: R = 0.659, p < 0.001; Fort Lauderdale: R = 0.339, p < 0.001). Nest sand microbiota were found to be more similar to hatched and unhatched egg microbiota than control sand and cloaca microbiota in both turtle species and beach comparisons ( Figure 2 and Table A4). Interestingly, nest sand and control sand samples were found to be significantly different from one another in both turtle species and beach analyses ( Figure 2 and Table A4).

| Differences between hatched and unhatched egg microbiota
One-way ANOSIM tests found significant differences in bacterial taxa between hatched and unhatched eggs in both turtle species  (Tables A5 and A6).
Pseudomonadota (formerly known as Proteobacteria [Oren & Garrity, 2021]) was found as the highest correlated phylum to the differences between hatched and unhatched eggs for both turtle species and beaches and was found in higher abundances in unhatched eggs except in green turtle analyses ( Figure 3 and Table A5). Actinomycetota (formerly known as Actinobacteria [Oren & Garrity, 2021]) and Bacillota (formerly known as Firmicutes [Oren & Garrity, 2021]) contributed higher abundances in unhatched eggs than hatched eggs (except for Bacillota in Hillsboro samples) while Bacteroidota contributed a higher abundance in hatched eggs than unhatched eggs ( Figure 3 and Table A5). SIMPER analyses found Pseudomonas as the highest correlated genus to the differences between unhatched and hatched egg microbiota for both turtle species and beaches and was found in higher abundances in unhatched eggs ( Figure 4 and Table A6). Alcaligenes also contributed a higher abundance in unhatched eggs than hatched eggs while Nitratireductor, Flavobacterium, Paenibacillus, and Sphingobacterium contributed higher abundances in hatched eggs ( Figure 4 and  ) showing the variability outside the upper and lower quartiles and outliers represented as black dots. Statistically significant differences, represented by the asterisks (*) were determined using the pairwise test in the "emmeans" package after the "best-fit" model was determined (p values can additionally be found in Table A3). Cloaca samples were significantly different in bacterial richness and diversity between turtle species and were only significantly different between beaches (loggerhead samples only) for bacterial richness. Control sand samples were significantly different in bacterial richness and diversity between turtle species and beaches, while nest sand samples were only significantly different in bacterial richness between beaches. Unhatched eggs were significantly different in diversity between turtle species, while hatched eggs exhibited no significant differences.

| Potential transmission source
Cloaca, sand, and egg samples were compared to determine whether there was a greater maternal or environmental effect on egg microbiota. All egg samples shared more unique ASVs with nest sand samples (3%-20%) than the cloaca (0.5%-2%) or control sand  (Table A8).  Table A9). Incubation length, high tide distance, and temperature at the bottom of nests were all found to positively affect bacterial species richness (t = 1.01, 3.13, and 1.48, respectively).

| Environmental factors shaping egg microbiota
Three environmental variables were retained with all interactive variables in the GLMM analysis of Shannon diversity, which strongly explained the variability in bacterial diversity (R 2 = 0.64; Table A9).
Clutch size, high tide distance, and temperature at the bottom of nests were all found to positively affect bacterial diversity (t = 3.65, 2.01, and 1.66, respectively). One environmental variable was

F I G U R E 3 Most abundant bacterial phyla bar chart comparison of egg samples for turtle species and beaches. The most abundant bacterial phyla (based on relative abundance) were identified separately for each turtle species (Loggerhead [a] and Green Turtle [b]) and for the loggerhead samples at each beach (Fort Lauderdale [c] and Hillsboro [d])
. Only bacterial phyla that had a relative abundance greater than 1% in either hatched or unhatched egg samples were selected for representation in these bar charts. All egg samples contained about 99% of the most abundant phyla identified. Loggerhead and both beach samples had a higher abundance of Pseudomonadota in unhatched egg samples. Both turtle species and Fort Lauderdale samples had a higher abundance of Bacillota in unhatched egg samples. Bacteroidota and Actinomycetota were in higher abundance in hatched eggs than unhatched eggs for both turtle species and beaches. Bdellovibrionota was only identified as an abundant bacterial phylum in Hillsboro samples and had a higher relative abundance in hatched eggs (1.01%) than unhatched eggs (0.04%).
retained with only the sample type-beach interactive variable in the GLMM analysis of Inverse Simpson's diversity, which moderately explained the variability in bacterial diversity (R 2 = 0.25; Table A9).
Sorting coefficient was found to negatively affect bacterial diversity (t = −0.24).
BIO-ENV analyses showed that loggerhead egg samples had more environmental variables (six) explaining the change in bacterial communities when compared to green turtles (four) (

| Hatched and unhatched egg microbiota comparisons
Our study was able to collect samples from nests with hatching success rates ranging from 37% to 100% (Table 1)

| Hatched egg microbiota-The putative "healthy microbiota"
Based on the findings of the present study, hatched eggs correlated with having a lower species richness (between 300 and 400 ASVs) F I G U R E 5 Unique ASV counts shared between sample types for Loggerhead (a), Green Turtle (b), Fort Lauderdale (c), and Hillsboro ( (He et al., 2017;Thomas et al., 2011).
Bacteroidota has been found to possibly contribute to the formation of chicken embryonic intestinal microbiota during egg development (Ding et al., 2022), and may be expected to play a similar role in sea turtles. Myxococcota and Bdellovibrionota are phyla known for their predatory lifestyles. Myxococcota can secrete diverse secondary metabolites as antimicrobial proteins and metabolites, which are presumed to aid them in predating a broad range of microbes, including bacteria and fungi (Furness et al., 2020). Bdellovibrionota, on the other hand, is an obligate Gram-negative predator (Li et al., 2021). In both cases, the higher abundance of predatory phyla may protect the turtle embryos from potential pathogens.
At the genus level, hatched eggs were correlated with higher abundances of Sphingopyxis, Pseudonocardia, Devosia, and Cohnella (Table A7). Sphingopyxis and Devosia are genera known for their degradation capabilities. Sphingopyxis have the potential to degrade a number of xenobiotics and other environmental contaminants, which helps them interact and survive in extreme environments (Sharma et al., 2021). Detoxification and degradation of organic pollutants have been identified as dominant functions of the genus Devosia (Talwar et al., 2020). The presence of bacteria capable of degrading environmental contaminants may protect the turtle embryos from being harmed during the incubation process. Pseudonocardia produce diverse secondary metabolites with antimicrobial bioactivities but are mostly known for their symbiotic relationship with fungus-growing ants in which they inhibit entomopathogens that infect the ants (Goldstein & Klassen, 2020). The association with sea turtle eggs may play a role in protecting the eggs from fungal pathogens, such as Fusarium spp., which have been previously found in unhatched eggs (Brofft Bailey et al., 2018;Sarmiento-Ramírez et al., 2010. Cohnella is a genus known for its cellulolytic or xylanolytic activities (Arneodo et al., 2019), whose relevance to sea turtle egg survival is yet to be determined. We posit that these compounds may be used to reduce root invasion into the eggshell from dune vegetation, which has been found to reduce hatching success in leatherback sea turtles (Conrad et al., 2011). 4.3 | Unhatched egg microbiota-The putative "unhealthy microbiota" Unhatched eggs were found to have a high species richness (>500 ASVs) within their microbiota, representing more variable bacterial assemblages than hatched eggs. At the phylum level, unhatched (presumed unhealthy) eggs were correlated with higher abundances of Pseudomonadota and Bacillota (Table A7). Pseudomonadota and Bacillota have previously been associated with disease suppression within the rhizosphere and have been previously identified in Fusarium-infected sea turtle eggs (Mendes et al., 2011;. The presence of certain Pseudomonadota (Betaproteobacteria, Gammaproteobacteria) has been suggested as a diagnostic for dysbiosis in humans due to their role in protein degradation and utilization of sugar and oxygen in the gut (Shin et al., 2015). Pseudomonadota has been previously suggested as an indicator of dysbiosis in sea turtles (Campos et al., 2018;Samuelson et al., 2020). Within the present study at the class level for Pseudomonadota, Gammaproteobacteria was found in higher abundance in unhatched eggs, while Alphaproteobacteria was found in higher abundance in hatched eggs ( At the genus level, unhatched eggs were correlated with higher abundances of Pseudomonas (Table A7). The genus Pseudomonas was identified as the major driver of the observed differences between hatched and unhatched egg samples, with a higher average relative abundance found in unhatched eggs (19.29%; variance = 7.34%) than in hatched eggs (1.10%; variance = 0.01%) (Figure 4 and Table A7).
Pseudomonas spp. have been suggested to play a strong role in sea turtle egg hatching failure (Capri et al., 2023) and have been previously isolated and identified in unhatched sea turtle eggs from other studies (Awong-Taylor et al., 2008;Craven et al., 2007;Keene, 2012;Wyneken et al., 1988). The genus Pseudomonas contains over 200 known taxa (Girard et al., 2021) that have high metabolic diversity, simple nutritional requirements, and wide temperature growth ranges (4°C-42°C) allowing them to inhabit a variety of environments (e.g., soil, water, plants, and association with larger organisms) as commensals and opportunistic pathogens (Chakravarty & Anderson, 2015). The diversity and adaptability of Pseudomonas may help explain why the taxon occurs widely in many environments and was found abundantly in unhatched eggs; however, we cannot be sure if the taxon caused the eggs to not hatch. Pseudomonas spp. have long been associated with proteinaceous food (i.e., eggs, milk, meat) spoilage under aerobic conditions (Raposo et al., 2016), so the presence of Pseudomonas may be due to the decomposition of the unhatched eggs. Future work could be done to try to establish the timeline in which Pseudomonas spp. enter the eggs to determine its functional role in sea turtle eggs.
Alcaligenes and Achromobacter were also found in higher abundance in unhatched eggs than hatched eggs (Figure 4). Both Alcaligenes and Achromobacter are known as opportunistic pathogens and are commonly found in soil, water, and intestinal tracts of vertebrates, such as loggerhead sea turtles (Busse & Stolz, 2006;Trotta et al., 2021). Alcaligenes spp. and Achromobacter spp. have recently been identified in sea turtle eggs, specifically green turtles (Candan & Candan, 2020), our study provides additional support to their presence and provides new insights into their higher abundances in unhatched eggs.

| Beach environment influence and tracking potential transmission source
The transmission of pathogens into sea turtle eggs is unknown, but we can hypothesize that transmission occurs either through (i) maternal transmission during the two-week formation period within the uterine tube and oviposition (Funkhouser & Bordenstein, 2013) or (ii) environmental transmission from sand surrounding the nest during the two-month incubation period (Craven et al., 2007). Our study found that 44%-49% of unhatched eggs and 61%-90% of hatched egg microbiota derived from the nest sand microbiota, suggesting that the nest sand environment played a stronger role in shaping both egg microbiota than the cloaca, which was estimated to contribute less than 1% to both hatched and unhatched egg microbiota ( Figure 6). Our results support the findings of Capri et al. Maternal and environmental transmission, however, are not necessarily mutually exclusive. Sea turtle egg microbiota may potentially stem from a "starter" community from the nesting female, but over time, succession of environmental microbes may occur, depending on the original community composition established maternally, its resilience to change, and random, chance effects.
Our study found that sea turtle nests host a different bacterial community than the original beach sand with only 46.55% of the nest microbiota being sourced from the control sand ( Figure 2). We hypothesize that the distinction between the two communities may be a result of the mucus excreted by the female during oviposition.
The potential antimicrobial properties, warm nest temperature (28.5°C-33.9°C in our study, Table 1), and added nutrients from the female mucus during oviposition may prime the nest sand and eggs to allow for the "starter" microbial community to colonize first during the initial stages of the incubation period. This would allow for a new microbial community to develop within the nest in place of the original beach microbial community. However, the cloaca was found to only account for 0.89% of bacterial introduction in nest sand samples suggesting that the change in nest sand composition from control sand cannot be directly attributed to the female cloaca. The "starter" microbial community within the nest from cloacal mucus may shift from the succession of environmental microbes over time due to the incubation length of sea turtle eggs (40-60 days).
There is also the potential for pathogens to be transmitted by multiple routes (Bright & Bulgheresi, 2010). The exact route of transmission for a pathogen may depend on the direct trade-offs or indirect fitness effects caused by the different routes, the environment the pathogen is in, or based on symbiotic relationships with other microbes (Antonovics et al., 2017;Russell, 2019). Therefore, the likelihood of mixed-mode transmission being used in sea turtle eggs appears high. For example, Pseudomonas, which was the most significant driver of differences between hatched and unhatched eggs, was present in all but three samples. Additionally, Pseudomonas spp. were found in low abundances in both cloaca (loggerhead = 0.21%, green turtle = 1.02%) and sand samples ( We found that 28%-37% of hatched egg and 24%-35% of unhatched egg ASVs did not match the cloaca, control sand, or nest sand ( Figure 5) and 1%-4% of hatched egg and 24%-48% of unhatched egg microbiota were determined to be from unknown sources ( Figure 6). Therefore, there is still the potential for many other sources of transmission (e.g., water [rain or ocean], vegetation [dune or Sargassum], marine debris, and sand macroorganisms) due to the different location of each nest or additional sources before oviposition (e.g., paternal or other maternal sources [i.e., not the cloaca]). Different sea turtle nesting beaches could also be exposed to unique wildlife and vegetation or have different proximities to waste outflows and harbors that can impact nest microbiota.
We found that geographic location alone did not shape sea turtle egg microbiota, however, different combinations of environmental factors between turtle species and beaches explained the bacterial differences in egg microbiota (Tables A9 and A10). The variation in environmental factors between turtle species may be attributed to nest site selection differences between loggerhead and green turtles.  Table 1). Dune distance was found to play the most significant role in shaping the bacterial differences in beach sand composition in BIO-ENV analyses. Nest distance from the high tide line was potentially not isolated as the best environmental factor in the BIO-ENV analysis because the tidal distance is constantly changing, while the dune distance is a more permanent variable.
However, high tide distance was found to significantly affect the bacterial richness and Shannon diversity in the GLMM analyses (Table A9).
Our hypothesis that the local beach environment has the largest effect in shaping sea turtle egg microbiota rather than the nesting mother's microbiota counters previous research that vertical (parent-offspring) symbiont transmission increases in terrestrial environments and decreases in aquatic environments (Russell, 2019 Table 1); thus, the nests have the opportunity to be exposed to periodic tidal washover and aquatic microbiota. Horizontal (environment-offspring) transmission of aquatic bacteria may increase with nests that are near the high tide line due to increased washover occurrences or from the introduction of pathogens from terrestrial run-off. Nests that are laid near the high tide line have the potential to also be introduced to high levels of fecal indicator bacteria, which have been found to exceed set colonyforming unit levels 2.475 times more during high tide than low tide on South Florida beaches (Aranda et al., 2016). The beach environment, particularly dune distance, was found to play a role in shaping the bacterial community differences within control sand samples. Similarly, nest sand communities can potentially be affected by the beach environment and cause egg bacterial differences, such as species richness and diversity, as we saw in our analyses (Table A9).
Differences in control sand microbiota between beaches were found to be mainly caused by the genera Sphingobacterium, Ochrobactrum, Achromobacter, and Nitratireductor, which all occurred in higher abundances in Fort Lauderdale than Hillsboro. Additionally, Sphingobacterium, Ochrobactrum, and Nitratireductor were all found in higher abundances in hatched eggs than unhatched eggs on both beaches ( Figure 4). This may explain why Fort Lauderdale had a significantly higher average hatching success rate (88.78%) when compared to Hillsboro (82.62%) overall in the 2021 nesting season for loggerheads (nonparametric two-tailed t-test: W = 323344, p < 0.001). Our findings suggest that the localized environmental conditions and bacteria at each nest may play an important role in shaping the egg microbiota and potentially affecting their hatching success, as previously suggested (Capri et al., 2023). Further research should be conducted to understand the role these bacteria play in egg-hatching success and their use in beach ecosystem dynamics. Note: ANOSIM R statistics comparing the mean of ranked dissimilarities between groups to the mean of ranked dissimilarities within groups were determined for all possible pairwise comparisons between sample types (cloaca, control sand, hatched egg, unhatched egg, and nest sand). ANOSIM analyses were completed separately by turtle species (loggerhead and green turtle) and beaches (Fort Lauderdale and Hillsboro) for loggerhead samples only. R statistics closer to "1.0" suggest dissimilarity between groups. Pairwise comparisons that were not found to be significant (p value) are indicated in red.

| CONCLUSIONS
T A B L E A5 SIMPER analyses for hatched and unhatched egg samples at the phylum-level. Note: SIMPER analyses between hatched and unhatched eggs were completed for each turtle species (loggerhead and green turtles) and at each beach (Fort Lauderdale and Hillsboro) for loggerhead samples only. ASVs were consolidated by phylum before analysis. The total percentage of dissimilarity between egg sample types, relative abundance per egg sample type, and top contributing bacterial phyla (with the percentage of contribution) were determined. Only the bacterial phyla with greater than 1% contribution were selected. The top bacterial phyla contributed a total of 30.50% to the total dissimilarity between hatched and unhatched eggs in loggerhead samples, 16.88% in green turtle samples, 28.38% in Fort Lauderdale samples, and 32.75% in Hillsboro samples.
T A B L E A6 SIMPER analyses for hatched and unhatched egg samples at the genus-level. Note: SIMPER analyses between hatched and unhatched eggs were completed for each turtle species (loggerhead and green turtles) and at each beach (Fort Lauderdale and Hillsboro) for loggerhead samples only. The total percentage of dissimilarity between egg sample types, relative abundance per egg sample type, and top contributing bacterial genera (with the percentage of contribution) were determined. Only the bacterial taxa with greater than 3% contribution were selected. Contributing bacterial taxa that were not found to be significant are indicated in red. The top genera contributed a total of 28.83% to the total dissimilarity between hatched and unhatched eggs in loggerhead samples, 11.59% in green turtle samples, 29.39% in Fort Lauderdale samples, and 30.94% in Hillsboro samples.
T A B L E A7 Relative abundances of bacteria identified in CAP analyses for hatched and unhatched eggs. Note: BIO-ENV correlations were determined for hatched and unhatched eggs for each turtle species (loggerhead and green turtles) and each beach (Fort Lauderdale and Hillsboro) for loggerhead samples only. The highest correlation (BIO-ENV correlation) and the set of environmental variables that go with it are given in the first two rows. The individual environmental variable with the highest correlation is also listed with its correlation in parentheses. All BIO-ENV analyses found significant correlations (p < 0.05) between the identified environmental variables and patterns in bacterial community composition.
F I G U R E A1 Sample-sized-based rarefaction (interpolation) and prediction (extrapolation) curves by sample type for each turtle species. Turtle species were analyzed separately: loggerhead (top plots) and green turtles (bottom plots). Plots on the left (q = 0) show bacterial taxa richness while plots on the right (q = 2) show Simpson diversity. Diversity was plotted against the number of ASVs ("individuals," sample size) based on sample types: cloaca (red), control sand (brown), hatched egg (green), nest sand (blue), and unhatched egg (purple). The solid portion of the slopes represents the rarefaction (interpolation) of each sample type, while the dashed portion represents the predicted values (extrapolation) of each sample type. A higher diversity value at which the slope reaches a plateau indicates a sample type having greater richness or diversity. Loggerhead samples had greater bacterial richness and diversity than green turtle samples.